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Developing an Atlas of Harmful Algal Blooms in the Red Sea: Linkages to Local Aquaculture

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Developing an Atlas of Harmful Algal Blooms in the Red Sea: Linkages to Local Aquaculture remote sensing Article Developing an Atlas of Harmful Algal Blooms in the Red Sea: Linkages to Local Aquaculture Elamurugu Alias Gokul 1, Dionysios E. Raitsos 2 , John A. Gittings 1 and Ibrahim Hoteit 1,* 1 Earth Science and Engineering (ErSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia; [email protected] (E.A.G.); [email protected] (J.A.G.) 2 Department of Biology, National and Kapodistrian University of Athens (NKUA), 15784 Athens, Greece; [email protected] * Correspondence: [email protected] Received: 11 September 2020; Accepted: 2 November 2020; Published: 11 November 2020 Abstract: Harmful algal blooms (HABs) are one of the leading causes of biodiversity loss and alterations to ecosystem services. The Red Sea is one of the least studied large marine ecosystems (LMEs), and knowledge on the large-scale spatiotemporal distribution of HABs remains limited. We implemented the recently developed remote sensing algorithm of Gokul et al. (2019) to produce a high-resolution atlas of HAB events in the Red Sea and investigated their spatiotemporal variability between 2003 and 2017. The atlas revealed that (i) the southern part of the Red Sea is subject to a higher occurrence of HABs, as well as long-lasting and large-scale events, in comparison to the northern part of the basin, and (ii) the Red Sea HABs exhibited a notable seasonality, with most events occurring during summer. We further investigated the potential interactions between identified HAB events and the National Aquaculture Group (NAQUA), Al-Lith (Saudi Arabia)—the largest aquaculture facility on the Red Sea coast. The results suggest that the spatial coverage of HABs and 3 the elevated chlorophyll-a concentration (Chl-a)(> 1 mg m− ; a proxy for high nutrient concentration), in the coastal waters of Al-Lith during summer, increased concurrently with the local aquaculture annual production over a nine-year period (2002–2010). This could be attributed to excessive nutrient loading from the NAQUA facility’s outfall, which enables the proliferation of HABs in an otherwise oligotrophic region during summer. Aquaculture is an expanding, high-value industry in the Kingdom of Saudi Arabia. Thus, a wastewater management plan should ideally be implemented at a national level, in order to prevent excessive nutrient loading. Our results may assist policy-makers’ efforts to ensure the sustainable development of the Red Sea’s coastal economic zone. Keywords: harmful algal blooms; Red Sea; remote sensing; aquaculture 1. Introduction In global marine ecosystems, harmful algal blooms (HABs) are known for the rapid increase of algal biomass and occasionally the production of toxins [1]. HABs are often associated with a broad range of ecological impacts, including increased mortalities of wild and farmed fish, displacement of indigenous species, and alterations to marine food webs, which ultimately have a detrimental effect on human societies [2,3]. HABs constitute a worldwide issue, which is constantly intensified in its frequency and spatial extent, primarily due to oceanic warming (climate change) and increasing anthropogenic pollution [4–7]. The Red Sea, a large marine ecosystem (LME) characterized by thriving coral reef complexes, high levels of biological diversity, and a growing aquaculture industry, is one such region that has been subjected to substantial impacts from HABs [8–17]. A recent study summarized historical HAB events that were previously reported by different field sampling programs in the Red Sea over the last Remote Sens. 2020, 12, 3695; doi:10.3390/rs12223695 www.mdpi.com/journal/remotesensing Remote Sens. 2020, 12, 3695 2 of 14 three decades [18]. The main phytoplankton functional types (PFTs) responsible for the reported HAB events were found to be dinoflagellates, raphidophytes, and cyanobacteria [18–20]. Besides traditional in situ approaches, Gokul et al. [17] developed and validated a remote sensing model to detect and map the spatial distribution of HABs associated with the aforementioned PFTs in the Red Sea. Several studies focusing on Red Sea HABs have enabled a deeper understanding of their spatiotemporal distribution, species toxicity levels (only possible through in situ measurements) and the underlying oceanographic mechanisms responsible for their outbreaks [12,17,18]. For instance, previous studies have conducted field sampling programs and reported several HAB species including Kryptoperidinium foliaceum, Noctiluca scintillans/miliaris, Heterosigma akashiwo, Pyrodinium bahamense var. bahamense, Ostreopsis sp. and the cyanobacterium Trichodesmium erythraeum [8–16]. Mohamed [18] documented the main environmental factors contributing to the dominance of these HAB species. Recently, a remote sensing algorithm was developed to investigate the spatial variability of these HAB species in the Red Sea, in accordance with previously reported events in the Red Sea [17]. However, studies on Red Sea HABs are limited with regards to the investigation of their large-scale spatial distribution, long-term temporal (interannual and/or seasonal) evolution, and interactions with local aquaculture industries. Such information is essential for the regional economy (i.e., fisheries, aquaculture, and tourism) and will support policy-makers to optimize the sustainable management of the Red Sea’s coastal economic zone. For instance, the National Aquaculture Group (NAQUA) located at the coastal city Al-Lith is the largest aquaculture facility in the Red Sea and is an important source of food and economic prosperity to the Kingdom of Saudi Arabia. Thus, routine monitoring of HABs over the relevant spatial–temporal scales is required in order to develop a more sustainable aquaculture operation and to improve coastal management strategies [11,20,21]. Here, we implemented the remote sensing algorithm developed by Gokul et al. [17] on a 15-year satellite-derived remote sensing reflectance (Rrs) dataset (2003–2017) to produce an atlas of HAB events in the Red Sea. Furthermore, we investigated the interactions between HAB events and Al-Lith’s NAQUA aquaculture facility. Our HAB Red Sea atlas will assist the policy-makers with regional decision-making and risk mitigation processes that are associated with environmental and aquaculture management. 2. Materials and Methods This study used the recently developed remote sensing algorithm of Gokul et al. [17] to develop an atlas of HABs in the Red Sea and then investigate their possible linkages to the largest aquaculture of the basin. This remote sensing algorithm was developed by applying high resolution satellite-derived Rrs observations and available in situ datasets to a second order derivative technique for detecting the presence/absence and mapping the spatial distribution of HABs in the Red Sea [17]. 2.1. Satellite Datasets Satellite remote sensing datasets of Rrs were acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Medium Resolution Imaging Spectrometer (MERIS). Satellite-derived observations of Chlorophyll-a concentration (Chl-a) were obtained from the Ocean Colour Climate Change Initiative (OC-CCI) of the European Space Agency (ESA). We obtained MODIS-Aqua and MERIS datasets from the NASA ocean color archive (https://oceancolor.gsfc.nasa.gov), whereas the OC-CCI dataset is available at http://www.esa-oceancolour-cci.org. First, MODIS-Aqua Level-3 mapped data of Rrs (for wavelengths 412, 443, 488, 531, 547, 667, and 678 nm) were acquired at a spatial resolution of 4 km, and an 8-day temporal resolution, for the Red Sea, covering a 15-year period spanning from 2003 to 2017. Secondly, we used Level-2 MERIS-derived Rrs data (for wavelengths 413, 443, 490, 510, 560, 620, 665, 681, and 709 nm) at a 1.2 km and 1-day resolution for a 9-year period (2002–2010), covering the region of Al-Lith, Saudi Arabia. The region of Al-Lith and its aquaculture facility, is located between 19◦12’0”N and 20◦6’0”N latitude and 40◦0’0”E and 42◦0’0”E longitude (see red box in Figure1). Previous studies have suggested that the aforementioned region is subject to the substantial nutrient loading from NAQUA aquaculture Remote Sens. 2020, 12, 3695 3 of 14 effluents [11,20,21]. Finally, version 4.1 of the ESA OC-CCI product was used. This product is comprised of merged and bias-corrected Chl-a data from MERIS, MODIS, Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), and the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite sensors. Level-3 mapped data of Chl-a were obtained at 4 km spatial resolution and 8-day temporal resolution for the period 2002 to 2010 in the Al-Lith region. We note that standard remote sensing algorithms may systematically overestimate Chl-a concentrations when compared with in situ measurements in the Red Sea. This is mainly due to excess chromophoric dissolved organic matter (CDOM) absorption per unit chlorophyll in this basin when compared with average global conditions [22]. However, Brewin et al. [23] and Racault et al. [24] have demonstrated that the OC-CCI derived Chl-a dataset exhibits a good agreement with independent in situ Chl-a datasets in coastal and open waters of the Red Sea. In addition, several studies have suggested that the OC-CCI product is characterized by higher data availability in comparison to single-sensor-based missions [24–26]. Thus, we are confident in using the OC-CCI derived Chl-a dataset for investigating interactions between HABs and local aquaculture in the coastal watersRemote Sens. of Al-Lith. 2020, 12, x FOR PEER REVIEW 4 of 14 Figure 1. Map showing the bathymetry of the Red Sea (acquired from the General Bathymetric Chart of Figure 1. Map showing the bathymetry of the Red Sea (acquired from the General Bathymetric Chart the Oceans (GEBCO_2014 Grid, version 20150318, http://www.gebco.net/)). The Red Sea is divided into of the Oceans (GEBCO_2014 Grid, version 20150318, http://www.gebco.net/)). The Red Sea is divided four different provinces, following Raitsos et al.
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